Senior Data Engineer (12+ Years Experience)

Role Summary

We are seeking a seasoned Senior Data Engineer with 12+ years of experience in designing, building, and managing scalable data pipelines and architectures. The role requires deep expertise in modern data engineering practices, cloud platforms, and enterprise-grade data solutions to support analytics, reporting, and strategic decision-making.

Key Responsibilities

• ETL/ELT Development: Design, build, and maintain robust ETL/ELT pipelines to ingest, transform, and deliver data from diverse sources.

• Data Lake & Warehouse Design: Architect and implement scalable data lakes and warehouses to support analytics and BI workloads.

• System Optimization: Optimize data processing frameworks for performance, scalability, and cost efficiency.

• Data Security: Implement security best practices, encryption, and role-based access controls.

• Data Governance: Ensure compliance with governance frameworks, data lineage, and quality standards.

• Collaboration: Work closely with data scientists, analysts, and business stakeholders to deliver reliable data solutions.

• Automation & CI/CD: Integrate DevOps practices for automated deployment and monitoring of data pipelines.

• Mentorship: Provide technical guidance and mentorship to junior engineers, fostering a culture of excellence.

Required Skills

• Programming Expertise: Advanced proficiency in Python, Scala, and/or Java for data engineering tasks.

• Cloud Platforms: Hands-on experience with AWS, Azure, or GCP for building cloud-native data solutions.

• Data Architecture: Strong knowledge of data modeling, warehousing, and distributed systems.

• Big Data Tools: Experience with Spark, Hadoop, Kafka, or equivalent frameworks.

• SQL Mastery: Expertise in writing optimized queries, stored procedures, and performance tuning.

• Visualization Support: Ability to prepare curated datasets for BI tools like Power BI or Tableau.

• Problem-Solving: Strong analytical and troubleshooting skills for complex data challenges.

Preferred Qualifications

• Experience with containerization (Docker, Kubernetes) for data workloads.

• Knowledge of real-time streaming architectures (Kafka, Kinesis, Event Hub).

• Familiarity with data mesh and lakehouse architectures.

• Exposure to machine learning pipelines and advanced analytics integration.

• Proven track record of leading enterprise-scale data engineering projects.